Multigrate: single-cell multi-omic data integration

Abstract

Single-cell multimodal omics technologies provide a holistic approach to study cellular decision making. Yet, learning from multimodal data is complicated because of missing and incomplete reference samples, non-overlapping features and batch effects between datasets. To integrate and provide a unified view of multi-modal datasets, we propose Multigrate. Multigrate is a generative multi-view neural network to build multimodal reference atlases. In contrast to existing methods, Multigrate is not limited to specific paired assays, and it compares favorably to existing data-specific methods on both integration and imputation tasks. We further show that Multigrate equipped with transfer learning enables mapping a query multimodal dataset into an existing reference atlas.